Spatial ecology of the stone marten in an Alpine area: combining camera-trapping and genetic surveys - ADDI
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Mammal Research (2021) 66:267–279
https://doi.org/10.1007/s13364-021-00564-9
ORIGINAL PAPER
Spatial ecology of the stone marten in an Alpine area: combining
camera-trapping and genetic surveys
A. Balestrieri 1,2 & A. Mosini 3 & F. Fonda 2 & M. Piana 3 & P. Tirozzi 4 & A. Ruiz-González 5,6 & E. Capelli 2 & M. Vergara 5 &
L. J. Chueca 5 & G. Chiatante 2 & C. Movalli 7
Received: 9 October 2020 / Accepted: 11 March 2021 / Published online: 24 March 2021
# The Author(s) 2021
Abstract
A species’ potential distribution can be modelled adequately only if no factor other than habitat availability affects its occur-
rences. Space use by stone marten Martes foina is likely to be affected by interspecific competition with the strictly related pine
marten Martes martes, the latter being able to outcompete the first species in forested habitats. Hence, to point out the environ-
mental factors which determine the distribution and density of the stone marten, a relatively understudied mesocarnivore, we
applied two non-invasive survey methods, camera-trapping and faecal-DNA based genetic analysis, in an Alpine area where the
pine marten was deemed to be absent (Val Grande National Park N Italy). Camera trapping was conducted from October 2014 to
November 2015, using up to 27 cameras. Marten scats were searched for between July and November 2015 and, to assess
density, in spring 2017. Species identification was accomplished by a PCR-RFLP method, while 17 autosomal microsatellites
were used for individual identification. The stone marten occurred in all available habitats (83% of trapping sites and 73.2% of
scats); nonetheless, habitat suitability, as assessed using MaxEnt, depended on four major land cover variables—rocky grass-
lands, rocks and debris, beech forests and chestnut forests—, martens selecting forests and avoiding open rocky areas. Sixteen
individuals were identified, of which 14 related to each other, possibly forming six different groups. Using capwire estimators,
density was assessed as 0.95 (0.7–1.3) ind/km2. In the study area, the widespread stone marten selected forested areas, attaining
density values like those reported for the pine marten in northern Europe and suggesting that patterns of habitat selection may
depend on the relative abundance of the two competing martens.
Keywords Camera-trapping . Non-invasive genetic sampling . Population density . Martes foina
Communicated by: Andrzej Zalewski
* A. Balestrieri
Introduction
alebls@libero.it
Affecting ecosystem function, structure and dynamics,
1
mesocarnivores play important roles in natural communities
Department of Environmental Sciences and Policy, University of
Milan, via Celoria 26, 20133 Milan, Italy
and are considered sensitive indicators of environmental health
2
and change in forested and aquatic habitats, particularly wher-
Department of Earth and Environmental Sciences, University of
Pavia, via Taramelli 22, 27100 Pavia, Italy
ever large carnivores have been driven to extinction by human
3
interference (Buskirk and Zielinski 2003; Roemer et al. 2009).
Valgrande Società Cooperativa - Studi, Opere e Servizi per
l’Ambiente, via alla Cartiera 91, 28923 Verbania Possaccio, Italy
Evaluating mesocarnivore distribution and abundance is thus
4
essential for investigating trophic cascades and predator-prey
Department of Earth and Environmental Sciences, University of
Milan-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
density-dependent relationships and conservation-aimed man-
5
agement (Williams et al. 2002).
Department of Zoology and Animal Cell Biology, University of the
Basque Country (UPV/EHU), C/ Paseo de la Universidad 7,
Nonetheless, with a few exceptions (red fox Vulpes vulpes,
01006 Vitoria-Gasteiz, Spain European badger Meles meles and Eurasian otter Lutra lutra),
6
Biodiversity Research Group, Lascaray Research Center, University
there is relatively little research on mesocarnivores (Brooke
of the Basque Country, UPV/EHU, Avda. Miguel de Unamuno, 3, et al. 2014). Among the mustelid family, which largely con-
01006 Vitoria-Gasteiz, Spain tribute to the diversity of European mesocarnivores, the stone
7
Val Grande National Park, Piazza Pretorio 6, 28805 Vogogna, Italy marten Martes foina has currently been understudied (Proulx
268 Mamm Res (2021) 66:267–279
et al. 2004), probably as an indirect consequence of its ab- Aiming to assess stone marten distribution, habitat se-
sence in the British Isles, which have long played a leader role lection and density in a pine marten-free, forested habitat,
in the investigation of mustelid ecology (e.g. Mcdonald 2002; we focused on the Val Grande National Park, a protected
O'Mahony et al. 2017; Mathews et al. 2018). Although the area in the Lepontine Alps, at the border between the west-
stone marten is widespread through much of continental ern and central sector of the mountain range, where both
Europe and central Asia—from Portugal in the west as far as available data for the whole Piedmont region (Sindaco and
north-western China in the east (Proulx et al. 2004)—, data on Carpegna 2010) and park rangers’ records indicated that
its spatial ecology are scarce (e.g. Abramov et al. 2006; Ruiz- pine marten occurrence may be null or negligible (only
Gonzalez et al. 2008), research having been focused on habitat two records were available: one in 1900 and one in 2006,
selection (reviewed by Virgós et al. 2012). To the best of our both outside the Park). We applied two non-invasive
knowledge, density data are reported by only three available methods, camera-trapping and faecal-DNA-based genetic
studies, conducted in either rural (Switzerland 0.7–2 ind/km2, sampling. Genotyping needed two steps, species identifi-
Lachat Feller 1993; Germany: 2 adult and 1.5 juvenile ind/ cation, which was necessary to exclude the samples be-
km2, Herrmann 2004) or urban areas (4.7–5.8 ind/km2, Herr longing to other sympatric mesocarnivores from further
et al. 2009) by means of radiotracking. In northern Italy, stone analyses, and microsatellite genotyping, to ascertain the
marten density has been assessed in an agricultural area using minimum number of individuals occurring in the study
camera-trapping and the Random Encounter Model proposed area (Ruiz-Gonzalez et al. 2008, 2013).
by Rowcliffe et al. (2008). Although population density was
similar to that recorded in rural Switzerland (0.96 ind/km2;
Ronchi 2016), the REM has been demonstrated to largely Study area
underestimate marten density (Balestrieri et al. 2016a).
Although being best adapted to warm climates (Proulx The Val Grande National Park (Piedmont region, Verbano-
et al. 2004), the stone marten has been recorded from sea level Cusio-Ossola province; 46° 01′ 45″ N 8° 27′ 34″ E) is the
up to 4200 m in Nepal (Oli 1994), while in Europe it occurs up largest wilderness area of the Alps (153.7 km2). The abandon-
to 2400 m a.s.l. on the Alps (Genovesi and De Marinis 2003). ment of traditional land use practices since the end of World
As other Martes species, the stone marten prefers forested War II has led to the decline of cultivated lands (meadows,
habitats (Virgós et al. 2012); in southern Europe, it is often pastures, chestnut orchards, crops and vineyards) from 59% of
associated to mosaics of forest and field patches (Sacchi and the whole area at the end of the 19th century to 5% in 1999
Meriggi 1995; Werner 2012; Vergara et al. 2017); nonethe- (Höchtl et al. 2005). Most previously cultivated areas current-
less, wherever available, forests are selected (Virgós and ly show various successional stages. Woods mainly consist of
Casanovas 1998; Ruiz-Gonzalez et al. 2015; Zub et al. 2018). beech Fagus sylvatica and chestnut Castanea sativa and cover
Habitat use by the stone marten is considered to be driven ca. 55% of the protected area. Mean yearly temperature and
by competition with the pine marten (Martes martes), which yearly rainfall are respectively 6.5 °C and 2300 mm with wide
should be able to outcompete the stone marten in forested variations depending on altitude above sea level, which ranges
habitats (Delibes 1983). More recently, it has been suggested between 250 and 2300 m.
that the strictly nocturnal stone marten may be more tolerant
of human disturbance than the cathemeral pine marten, and
thus may manage better than the latter in rural habitats Materials and methods
(Balestrieri et al. 2019). Nonetheless, in the Iberian
Peninsula, southwards of the southern edge of pine marten Several studies have suggested that the simultaneous use of
distribution, the stone marten is a mainly forest-dwelling multiple survey methods may provide a more complete as-
species, which selects low human density areas (Virgós sessment of mammal diversity (Silveira et al. 2003; Li et al.
and Casanovas 1998). 2012; Croose et al. 2019). Mustelids are elusive and their
Currently, forests are more widespread in mountainous densities being typically low, large areas need to be sampled
areas than in European lowlands: on the Alps, wood cover to assess their distribution and habitat preferences.
has progressively increased since the 1960s, following the Moreover, Martes spp. are very similar and their field signs
abandonment of low-intensity farming and livestock rearing cannot be distinguished by eye (Davison et al. 2002), mak-
(Falcucci et al. 2007), with a positive effect on forest-dwelling ing their monitoring even more challenging. To overcome
species (MacDonald et al. 2000). Pine- and stone marten are these hindrances, we applied two non-invasive methods,
sympatric throughout the Eastern Italian Alps, while, accord- which, based on the characteristics of both the study area
ing to available data, the latter would be by far most wide- and target species, were deemed to offer the best balance
spread than the pine marten in the central part of the mountain between cost-effectiveness and monitoring efficiency
range (Fonda 2019). (Roberts 2011; Balestrieri et al. 2016a, 2016b).
Mamm Res (2021) 66:267–279 269 Camera-trapping Faecal DNA-based species identification The study area was monitored by digital scouting camera- To supplement the data on stone marten distribution collected traps (Acorn II LTL 5210 with Passive Infra-Red motion sen- by camera-trapping, non-invasive genetic sampling was con- sor), tied to trees 30–50 cm above the ground level and set to ducted between July and November 2015. Fresh scats were record 30-s-long videoclips, with no interval between two searched for by two surveyors along linear transects coincid- successive recordings. Camera-trap sites were georeferenced ing with paths (N = 23; mean length = 7.2 km, min-max: 2.5– and superimposed on digital maps. We used an active survey 14.8 km). In the north-south corridor transects were surveyed design, attracting animals into the detection zone of the cam- monthly, while those in the rest of the protected area were era trap by placing scent lures (cat food in carnivore-proof sampled mostly once, and all samples were georeferenced containers) in front of camera-traps (ca. 5 m away). The use by a GPS. A small portion (ca. 1 cm) of each scat suspected of videoclips and lures aimed to improve the opportunity of to belong to Martes spp. based on size and morphology (see observing the distinctive morphological traits (van Maanen Remonti et al. 2012) was picked up using sticks, stored in 2013) of martens. In monochrome images, the three more autoclaved tubes containing ethanol 96% and preserved at – conspicuous features are the shape and position of the ears, 20 °C until processed. the paler colour of chest and thighs respect to forelimbs, hocks DNA was isolated using the QIAamp DNA Stool Mini Kit and tail in the stone marten and its chunkier overall silhouette. (Qiagen) according to the manufacturer’s instructions and spe- All videos of Martes spp. were subjected to a blind identifi- cies identification was accomplished by a PCR-RFLP method. cation procedure by three experienced researchers (AM, FF and Two primers amplify the mtDNA from Martes martes, M. foina AB) and discordant records were discarded. Capture indepen- and four Mustela species; then, the simultaneous digestion of dence was achieved by considering consecutive records of the amplified mtDNA by two restriction enzymes (RsaI and same species at the same site within a 30-min interval as a HaeIII) generates different restriction patterns for each mustelid single event (Kelly and Holub 2008; Monterroso et al. 2014). species, providing for an effective genetic identification of sym- From October 2014 to April 2015, the whole study area, patric marten species (Ruiz-Gonzalez et al. 2008, 2013). between 450 and 1720 m a.s.l., was surveyed by deploying 21 camera-traps as regularly as possible, depending on accessi- Individual identification by microsatellite genotyping bility (mean inter-trap distance ± SD = 2.6 ± 1.3 km; Fig. 1). In winter (Dec-Apr), only 10 trap-sites were activated, avoiding To assess the minimum number of stone marten individuals, a avalanche-prone sections. Between July and November 2015, specific sampling was carried out in the lower Pogallo valley, we focused on the north-south corridor formed by the valleys from April to June 2017, as to record only resident adult indi- “de il Fiume” and “Pogallo”. Camera-traps (N = 27) were viduals (i.e. after juvenile dispersal and before kits-of-the year deployed within a 1 × 1 km grid superimposed on the start marking; Libois and Waechter 1991). By superimposing kilometric grid of digitalized, 1:10,000 Regional Technical a 1 × 1 km grid on 1:10,000 maps, we identified 24, 1-km2 Maps, aiming to set each camera as much as possible in the contiguous sub-areas with adequate paths and accessibility to centre of the grid mesh and sample the most representative the major habitats and altitude belts (500–1000, 1001–1500 habitat (mean inter-trap distance ± SD = 1.0 ± 0.3 km; Fig. 1). and 1501–2000 m a.s.l.). Grid mesh size was based on avail- Variation over sampling periods in each species’ encounter able data on both stone- and pine marten density (0.1–3.16 rates was tested by the chi-squared (χ2) test. ind/km2; Marchesi 1989; Zalewski and Jedrzejewski 2006; Fig. 1 Camera-trapping sites and transects (coinciding with paths) surveyed in the Val Grande National Park
270 Mamm Res (2021) 66:267–279
Balestrieri et al. 2016a), aiming to lower the chance of missing Estimation of population size and kinship from
those individuals whose home ranges may fall in un-sampled genetic data
areas (Kays and Slauson 2008). Each transect was surveyed
twice (total length = 83.3 km), with a gap of 15–20 days Population size was assessed by capwire estimators (Miller
between visits. et al. 2005), an urn model developed expressly for faecal
All samples were georeferenced and stored at – 20 °C in DNA-based sampling which provides reliable estimates also
autoclaved tubes containing 96% ethanol until processed. for small populations and has been used to estimate popula-
Faecal DNA samples were genotyped using a multiplex panel tion size in several species (e.g. Arrendal et al. 2007;
of 17 autosomal microsatellite markers including 10 species- Sugimoto et al. 2014). To obtain the maximum likelihood
specific microsatellites (Mf 1.1, Mf 1.11, Mf 1.18, Mf 1.3, Mf estimate (MLE) of population size, data were fitted to either
2.13, Mf 3.2, Mf 3.7, Mf 4.17, Mf 8.7, Mf 8.8; Basto et al. the Equal Capture model, for which all individuals were as-
2010) and 7 additional markers described in closely related sumed to have an equal probability of being sampled, or Two-
mustelids (Ma1, Davis and Strobeck 1998; Mel1, Bijlsma Innate Rates model, assuming that the population contained a
et al. 2000; MLUT27, Cabria et al. 2007; Mvis072, Fleming mixture of easy-to-capture and difficult-to-capture individ-
et al. 1999; Mvi57, O’Connell et al. 1996; MP0059, Jordan uals. The fit of the two models was compared using a
et al. 2007; Lut453, Dallas et al. 2003), previously used in Likelihood Ratio Test (LRT) and the p-value was calculated
Martes spp. studies (Ruiz-González et al. 2013; Vergara et al. by using a parametric bootstrap approach to estimate the dis-
2015) and readapted for degraded faecal nDNA analysis (Ruiz- tribution of the LRT for data simulated under the less-
González et al. 2013). The forward primers, labelled with the parameterized Equal Capture model (Pennell et al. 2013).
dyes 6-FAM, NED, PET and VIC, were used in four PCR Confidence intervals (CI) for population size were estimated
multiplex reactions modified from Vergara et al. (2015) using a parametric bootstrap approach (Miller et al. 2005).
(Mult-A: Mlut27, Mel1, Mf1.1, Mf4.17; Mult-B: Lut453, Genetic relatedness and sibling analyses were calculated by
Ma1, Mf1.18, Mf3.7, Mf8.8, Mp0059; Mult-C: Mf1.11, ML-RELATE (Kalinowski et al. 2006) which uses a maxi-
Mf2.13, Mf3.2, Mvi072; Mult-D: Mf1.3, Mf8.7, Mvi-57). mum likelihood method to compute pair-wise genetic related-
To lower the probability of retaining false homozygotes ness (Rxy). Sibship analysis was conducted using COLONY
or false allele errors, a multitube-approach of 4 independent 2.0.4 (Jones and Wang 2010), with the typing error rate set at
replicates was used (Taberlet et al. 1996), followed by strin- 0.01. This approach considers the likelihood of the entire ped-
gent criteria to construct consensus genotypes (i.e. accepting igree, as opposed to relatedness on a pair-wise basis.
heterozygotes if the two alleles are recorded in ≥ 2 replicates
and homozygotes if a single allele is recorded in ≥ 3 repli-
cates) (e.g. Frantz et al. 2003; Brzeski et al. 2013). Briefly, Species distribution modelling
DNA quality was initially screened by PCR-amplifying each
DNA sample four times at four loci (Mult-A) and only sam- Land cover variables, extracted from available maps of forest-
ples showing > 50% positive PCRs were further amplified ed areas of Piedmont region (Table 1; IPLA 2016), were re-
four times at the remaining 12 loci. Samples with ambiguous sampled to a common resolution of 1 × 1 km cell size, using
results after four amplifications per locus or with < 50% QuantumGIS 2.16.3. To test for multi-collinearity among var-
successful amplifications across loci were not considered iables, the Variance Inflation Factor (VIF) was calculated
reliable genotypes and discarded (Ruiz-González et al. (Table 1), VIF values > 3 indicating highly correlated
2013). Multiplex PCR products were run on an ABI
(Foster City, CA) 3130XL automated sequencer (Applied
Biosystems), with the internal size standard GS500 LIZ™ Table 1 Land cover
variables used to Land cover variables % VIF
(Applied Biosystems) and fragment analysis was conducted determine the spatial
using the ABI software Genemapper 4.0. To test the dis- distribution of the stone Beech forests 40.5 2.98
crimination power of our microsatellite set, we computed marten in the Val Grande Chestnut forests 12.4 2.44
the probability of identity (PID) by GIMLET, using the National Park. Data are Rocky grasslands 10.7 1.15
expressed as percent
unbiased equation for both small sample size and siblings. cover in a 1 × 1 km grid; Scrublands 10.0 1.93
The more conservative PID for full-sibs (PID-Sib) was esti- the Variance Inflation Sub-Alpine Scrublands 7.5 1.59
mated as an upper limit to the probability that pairs of indi- Factor (VIF) was calcu- Grasslands 6.5 1.58
viduals would share the same genotype. Consensus geno- lated to test for multi-
Rocks and debris 5.5 1.34
collinearity among
types from four replicates were reconstructed using variables Coniferous forests 4.8 2.44
GIMLET, which was also used to estimate genotyping er- Other deciduous forests 2.0 1.21
rors: allelic dropout (ADO) and false alleles (FA) (Taberlet Urban areas 0.1 1.22
et al. 1996; Pompanon et al. 2005).Mamm Res (2021) 66:267–279 271
predictors (Fox and Monette 1992; Zuur et al. 2010; Wilson trap-days). The species was recorded at 83% of trapping sites
et al. 2012). (Table 2), occurring in all available habitats, up to 1850 m
Species Distribution Models (SDMs) were developed using above sea level (Fig. 2). The pine marten was recorded for
the MaxEnt algorithm (Phillips et al. 2006), a widely used meth- the first time in the north of the protected area in October
od which applies the principle of maximum entropy to predict 2014; throughout the study period, a total of 16 independent
the potential distribution of species from presence-only data events occurred at six different sites (12.5%; Table 2). The use
(Phillips and Dudík 2008; Elith et al. 2011), and has proved of baits and videos allowed the reliable identification of the
efficient for assessing habitat suitability for the stone marten two species for 79% of trapping events. For both martens,
(Vergara et al. 2016). Models were fitted to independent pres- encounter-rates did not differ among sampling periods (χ2 =
ence data, collected between October 2014 and June 2017, and 3.37, 2 df, P = 0.18 and χ2 = 1.10, 2 df, P = 0.58, respective-
an equal number of randomly selected background points ly). The mesocarnivore community also included the red fox
(Drake 2014). To ensure more ecologically realistic response (one video-clip per 28.1 trap-days) and European badger (one
curves, MaxEnt was run using only linear and quadratic features video-clip per 135.2 trap-days).
(Bateman et al. 2012, 2016) and default values for all the other To maximise the cost effectiveness of genetic analyses, the
parameters (maximum number of iterations = 5000; conver- apparently “less fresh” faecal samples—55 out of the 167
gence threshold = 10-5; multiplier regularization = 1). To assess faecal samples collected between July and September
the relative contribution of each variable, a jackknife test was 2015—were discarded. Of the remaining 112 samples, 96
used (Phillips et al. 2006). Particularly, we estimated the arith- (83.9%) could be assigned to a Martes species by the PCR-
metic mean between the percent contribution and permutation RFLP analysis—namely 82 to the stone marten and 12 to the
importance, two measures which define the contribution of each pine marten—, while 16 samples did not amplify.
variable to the final model (Elith et al. 2011; Meyer et al. 2014).
To obtain the best model, all variables with importance < 5%
were removed (Brambilla et al. 2013; Warren et al. 2014). Density and kinship as assessed by the genetic
Model accuracy was analysed by the area under the curve of sampling
the receiver operating characteristic (ROC) (Pearce and Ferrier
2000; Fawcett 2006). To assess the suitable area for the stone To assess stone marten density, 99 out of 128 faecal samples
marten, we converted the continuous suitability map into a bi- collected in spring 2017 were selected for microsatellite ge-
nary (suitable/unsuitable) classification, using the “equal train- netic analyses based on their “freshness”. The multiplex
ing sensitivity and specificity” threshold (ETSS; Lantschner screening test was not passed by 55 samples, which were then
et al. 2017). Mann-Whitney’s test was used to compare the discarded. Full multilocus microsatellite genotypes were ob-
environmental variables within stone marten positive cells tained for the remaining 44 samples, of which 41 were
(use) to the background (availability). All statistical analyses assigned to the stone marten, two to the pine marten and one
were carried out by R 3.4.3 packages raster (Hijmans et al. to Mustela sp.
2014), sp (Pebesma and Bivand 2011), usdm (Naimi 2017) The average proportion of positive PCRs (calculated from
and dismo (Hijmans et al. 2011). correctly and fully genotyped samples) was 82% and varied
among loci from 70 to 100%. The observed average error rates
across loci were ADO = 0.241 and FA = 0.021, while the
Results number of alleles per locus ranged from 3 to 10 (mean
5.83). Average, non-biased observed and expected heterozy-
Distribution gosities (Ho and He) were 0.6 and 0.62 respectively. PID
analysis showed that the set of 17 loci would produce an
A total trapping effort of 4539 camera trap-days allowed re- identical genotype with a probability of 1.54 × 10−9, and with
cording of stone martens 362 times (one video-clip per 12.5 a probability of 3.43 × 10−4 for a full-sib.
Table 2 Sampling effort
(expressed as number of trap- Period Site Trap- N recods % positive sites %ID
days) and results of camera- days
trapping in the Val Grande M. foina M. martes M. foina M. martes
National Park (VGNP)
X-XI 2014 VGNP 1176 100 4 80.9 9.5 73.2
XII-IV 2015 VGNP 1200 106 6 100 20 81.0
VII-IX 2015 Pogallo valley 2163 156 6 77.8 14.8 83.1
Total 4539 362 16 83.0 12.5272 Mamm Res (2021) 66:267–279
Fig. 2 Distribution of stone marten records in the Val Grande National Park
After a regrouping procedure (i.e. pairwise comparison of environmental variables (Fig. 4), of which four provided the
the different genotypes obtained), we identified 16 stone mar- major contributions: rocky grasslands (31.67% importance),
ten individual genotypes with a complete multi-locus profile. rocks and debris (24.63%), beech forests (16.64%) and chest-
The average number of detections (re-samplings) per individ- nut forests (10.63%). The first had a non-linear quadratic ef-
ual was 2.6 (min-max 1–6), with six individuals detected only fect, with the highest suitability at intermediate values of per-
once and two individuals recorded six times. Using capwire cent cover, although always > 0.4 (Fig. 4). Open rocky areas
estimators, population size was assessed as 21 (CI = 15 – 28) showed a negative trend, while suitability for the stone marten
individuals; including only the cells for which at least one increased linearly for the other two variables (Fig. 4). The
sample was genotyped (N = 22), density was assessed at discriminatory ability of the MaxEnt model was sufficient
0.95 (0.7–1.3) ind/km2. (AUC = 0.74). Following the reclassification analysis based
Except for individuals 2 and 4, all stone martens were re- on ETSS = 0.47, 63.9% of the study area resulted suitable for
lated to each other (14 half-sib pairs), individuals 3 and 5, 5 the stone marten (Fig. 5), which, according to univariate anal-
and 8, 6 and 13; and 11 and 16 being first order relatives (i.e. ysis, selected beech- and other deciduous forests, while
parent/offspring or full-sib dyad). Additionally, using ML- avoided rocky and scree areas (Table 3).
Relate, 13 full-siblings were acknowledged among the 16
stone martens. The two pine martens identified in the area
did not result related to each other.
If we assume that mountain ridges that border the valleys Discussion
coincide with the range limits of males, in the study area it was
possible to identify six clusters consisting of 1–3 individuals Associative modelling approaches, such as SDMs, which
(Fig. 3). consider the target species locations to be representative
of ideal habitat conditions (in the multidimensional space
described by the chosen variables), assess adequately the
Habitat suitability species potential distribution only if no other factor plays
a major role in determining its occurrences (Gough and
Habitat suitability, as assessed using 161 independent records Rushton 2000). For the stone marten, interspecific com-
of the stone marten in the protected area, depended on six petition with the strictly related pine marten has beenMamm Res (2021) 66:267–279 273 Fig. 3 Distribution of genotyped, individual stone martens in the Val Grande National Park claimed to affect both space use (Delibes 1983) and diet interspecific competition may play a negligible role in shaping (Gazzola and Balestrieri 2020), suggesting that pine mar- habitat use by the stone marten, although, when sympatric, the ten dominance may be a major biotic constraint of stone pine marten usually dominates in forested habitat as those marten’s niche. forming the bulk of our study area. In the VGNP, the results of both camera-traps and genetic Suitability models confirmed the preference of the surveys were consistent with the occasional record-based stone marten for a mosaic of forested areas, particularly framework, providing straightforward evidence of the wide- broad-leaved forests, which likely offer both food re- spread occurrence of the stone marten and patchy distribution sources and cover from predators, and rocky grassland of the pine marten. If we assume that the frequency of occur- areas, which may offer cavities which are both safe and rence of records is an index of the relative abundance of both thermal regulated resting sites (Birks et al. 2005; Virgós species (Gese 2001; Carbone et al. 2001), the stone marten et al. 2012). Pine martens, which usually nest on trees, stood out also in terms of numbers. Low pine marten abun- in winter, in response to extreme cold, frequently use dance may depend on the recent recolonisation of this sector cavities at ground-level (Brainerd et al. 1995; Zalewski of the Alpine range: in the last decade, the number of roadkills 1997). As the stone marten prefers warm climates has progressively increased and some camera-trapping re- (Vergara et al. 2016) and is also less arboreal than the cords have been collected in the western and northern parts pine marten (Goszczyński et al. 2007), in the study area of the province (Mosini and Balestrieri 2017), suggesting that may find suitable shelter sites in rocky crevices. Cold the pine marten may be reinforcing its occurrences on the Alps air temperatures and lack of cover would also explain as well as it is expanding in lowland areas of NW Italy the negative effect of “rock and debris” on habitat suit- (Balestrieri et al. 2015, 2016b). Whatever the reason, the large ability for the stone marten, as this habitat mostly coin- numerical difference recorded suggests that currently cides with the top of mountain ridges.
274 Mamm Res (2021) 66:267–279 Fig. 4 Response curves of the main land cover variables affecting stone marten distribution in the Val Grande National Park Detection probability may vary with the same covariates areas of allopatry. In our study area, small mammals that affect occurrence probability, leading to biased esti- (Clethrionomys glareolus, Glis glis, Apodemus sp.) formed mates of their importance in determining occupancy the bulk of stone marten diet (Balestrieri et al. 2018), suggest- (Yackulic et al. 2013). As, using camera-traps, encounter- ing that fruit availability did not affect the use of space. rates were constantly high throughout the study period and In the last two decades, faecal DNA-based genotyping has scat-based genetic sampling of stable marten populations proven an effective non-invasive method for estimating pop- has been demonstrated to provide detection probabilities ulation size for several elusive species, including mustelids next to 1 (Balestrieri et al. 2015), we are reasonably con- (e.g. pine marten, Ruiz-González et al. 2013; O’Mahony fident in the performance of our SDMs. et al. 2012; Sheehy et al. 2014; Eurasian otter, Arrendal Besides competition with the pine marten, a further factor et al. 2007; Vergara et al. 2014). In our study area, mean stone that has been reported to affect stone marten distribution and marten density fell within the range recorded in rural abundance is the availability of fleshy fruits (Mortelliti and Switzerland (Lachat Feller 1993) and was consistent with Boitani 2008; Virgós et al. 2010), the latter being considered the density of closely related pine marten in areas on northern the most frugivorous mesocarnivore (Virgós et al. 2012). Europe showing similar climatic conditions (0.6-0.7 ind/km2; Nonetheless, recently Gazzola and Balestrieri (2020) reported Zalewski and Jedrzejewski 2006). Assuming that topograph- that frugivory in the stone marten may depend on competition ical constraints drive home range size and shape, boundaries with the pine marten, the first preying mostly on rodents in tending to coincide with ridges (Powell and Mitchell 1998;
Mamm Res (2021) 66:267–279 275
Fig. 5 Habitat suitability map for the stone marten in the Val Grande National Park
Monterroso et al. 2013), stone marten individuals may be split 0.7; Kyle et al. 2003; Pertoldi et al. 2008), while was
into six groups, each consisting of 2-3 individuals, in agree- slightly higher than that reported for the stone marten
ment with the species’ intra-sexual spacing pattern (Powell (Iberian Peninsula: Ho = 0.49, Vergara et al. 2015;
1978; Genovesi et al. 1997). Eastern France: Ho = 0.55, Larroque et al. 2016;
As recorded for both pine martens (Balestrieri et al. Poland: Ho = 0.52, Wereszczuk et al. 2017).
2016a) and otters (Vergara et al. 2014), most individuals By applying two non-invasive methods, we pointed out that,
were related to each other, suggesting rapid population as reported for feeding habits (Monterroso et al. 2016), in areas
renewal and that dispersion occurs on relatively short dis- of sympatry with the pine marten patterns of habitat selection
tances. Heterozygosity matched with average values for depend on the relative abundance of the two competing species.
the pine marten in continental Europe (min-max 0.56– Being dominant, in the study area, the widespread stone marten
Table 3 Comparison (Mann-
Whitney test) of mean (± standard Land cover variables Use (%) Availability (%) p-value
deviation) percent land covers
between cells positive to the stone Mean SD Min- Mean SD Min-
marten (use) and the background max max
(availability). Significant (p <
0.05) differences between use and Beech forests 45.08 38.85 0–100 14.98 37.07 0–100 0.004
availability are shown in bold Other deciduous forest 3.45 8.12 0–44.46 2.79 12.86 0–100 0.007
Rocks and debris 1.89 4.97 0–28.63 6.58 13.30 0–80.58 0.049
Urban areas 0.23 0.93 0–5.12 0.08 0.54 0–4.87 0.124
Chestnut forests 17.44 30.88 0–98.71 12.44 27.42 0–100 0.170
Grasslands 5.04 12.25 0–59.59 7.87 19.98 0–96.49 0.409
Rocky grasslands 47.73 15.17 0–76.54 33.66 20.19 0–100 0.494
Coniferous forests 4.59 16.21 0–81.75 4.55 13.76 0–85.10 0.656
Sub-Alpine scrublands 6.89 15.62 0–82.94 7.88 18.09 0–98.65 0.705
Scrublands 5.76 12.53 0–66.77 12.78 22.53 0–100 0.755276 Mamm Res (2021) 66:267–279
selected forested areas, attaining density values like those re- Basto MP, Rodrigues M, Santos-Reis M, Bruford MW, Fernandes CA
(2010) Isolation and characterization of 13 tetranucleotide microsat-
ported for the pine marten in northern Europe. Further studies
ellite loci in the stone marten (Martes foina). Conserv Genet Resour
are needed to confirm the intra-sexual territorial behaviour and 2(S1):317–319
determine how competition with the pine marten affects stone Bateman BL, Van Der Wal J, Williams SE, Johnson CN (2012) Biotic
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National Park, as part of the project “Monitoraggio della biodiversità
with both short-term variability and long-term average climate data.
animale in ambiente alpino” (Monitoring of animal biodiversity on the
Ecol Appl 26:2720–2731
Alps). We are grateful to A. Biondo, F. Canepuccia, L. Caviglia, G.
Bijlsma R, Van de Vliet M, Pertoldi C, Van Apeldoorn RC, Van de
Cristiani, M. Dresco, E. Galbiati, D. Morisetti, D. Ramoni, D. Sabatini,
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S. Torniai, F. Zucca (Carabinieri Command for Forest Protection), M.
Meles meles. Mol Ecol 9:2216–2217
Gilardi and L. Ricci (graduate students), for their help with field work.
This paper is gratefully dedicated to the memory of the late Nicola Birks JDS, Messenger JE, Halliwell EC (2005) Diversity of den sites used
Saino (University of Milan), who supported the research throughout the by pine martens Martes martes : a response to the scarcity of arbo-
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Brainerd SM, Helldin J-O, Lindström ER, Rolstad E, Rolstad J, Storch I
(1995) Pine marten (Martes martes) selection of resting and denning
Funding Open access funding provided by Università degli Studi di sites in Scandinavian managed forests. Ann Zool Fenn 32:151–157
Milano within the CRUI-CARE Agreement.
Brambilla M, Bassi E, Bergero V, Casale F, Chemollo M, Falco R,
Open Access This article is licensed under a Creative Commons
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Attribution 4.0 International License, which permits use, sharing, adap- distribution and potential overlap between Boreal Owl Aegolius
tation, distribution and reproduction in any medium or format, as long as funereus and Black Woodpecker Dryocopus martius: implications
you give appropriate credit to the original author(s) and the source, pro- for management and monitoring plans. Bird Conserv Int 23:502–
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